From aebe937710ced03d03f73ab23f410f29685655c1 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 11 Aug 2016 18:54:24 +0000
Subject: [PATCH] what do you even write here?
---
src/darknet.c | 174 ++++++++++++++++++++++++++++++++++++++++++++++++++++------
1 files changed, 156 insertions(+), 18 deletions(-)
diff --git a/src/darknet.c b/src/darknet.c
index 7814611..c367abf 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -6,14 +6,16 @@
#include "utils.h"
#include "cuda.h"
#include "blas.h"
+#include "connected_layer.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
+extern void run_voxel(int argc, char **argv);
extern void run_imagenet(int argc, char **argv);
extern void run_yolo(int argc, char **argv);
-extern void run_swag(int argc, char **argv);
+extern void run_detector(int argc, char **argv);
extern void run_coco(int argc, char **argv);
extern void run_writing(int argc, char **argv);
extern void run_captcha(int argc, char **argv);
@@ -21,6 +23,13 @@
extern void run_dice(int argc, char **argv);
extern void run_compare(int argc, char **argv);
extern void run_classifier(int argc, char **argv);
+extern void run_char_rnn(int argc, char **argv);
+extern void run_vid_rnn(int argc, char **argv);
+extern void run_tag(int argc, char **argv);
+extern void run_cifar(int argc, char **argv);
+extern void run_go(int argc, char **argv);
+extern void run_art(int argc, char **argv);
+extern void run_super(int argc, char **argv);
void change_rate(char *filename, float scale, float add)
{
@@ -82,6 +91,41 @@
save_weights(sum, outfile);
}
+void speed(char *cfgfile, int tics)
+{
+ if (tics == 0) tics = 1000;
+ network net = parse_network_cfg(cfgfile);
+ set_batch_network(&net, 1);
+ int i;
+ time_t start = time(0);
+ image im = make_image(net.w, net.h, net.c);
+ for(i = 0; i < tics; ++i){
+ network_predict(net, im.data);
+ }
+ double t = difftime(time(0), start);
+ printf("\n%d evals, %f Seconds\n", tics, t);
+ printf("Speed: %f sec/eval\n", t/tics);
+ printf("Speed: %f Hz\n", tics/t);
+}
+
+void operations(char *cfgfile)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ int i;
+ long ops = 0;
+ for(i = 0; i < net.n; ++i){
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ ops += 2l * l.n * l.size*l.size*l.c * l.out_h*l.out_w;
+ } else if(l.type == CONNECTED){
+ ops += 2l * l.inputs * l.outputs;
+ }
+ }
+ printf("Floating Point Operations: %ld\n", ops);
+ printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
+}
+
void partial(char *cfgfile, char *weightfile, char *outfile, int max)
{
gpu_index = -1;
@@ -141,30 +185,48 @@
save_weights(net, outfile);
}
-void normalize_net(char *cfgfile, char *weightfile, char *outfile)
+void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
- if(weightfile){
+ if (weightfile) {
load_weights(&net, weightfile);
}
- int i, j;
- for(i = 0; i < net.n; ++i){
+ int i;
+ for (i = 0; i < net.n; ++i) {
layer l = net.layers[i];
- if(l.type == CONVOLUTIONAL){
- net.layers[i].batch_normalize=1;
- net.layers[i].scales = calloc(l.n, sizeof(float));
- for(j = 0; j < l.n; ++j){
- net.layers[i].scales[i] = 1;
- }
- net.layers[i].rolling_mean = calloc(l.n, sizeof(float));
- net.layers[i].rolling_variance = calloc(l.n, sizeof(float));
+ if (l.type == CONVOLUTIONAL && l.batch_normalize) {
+ denormalize_convolutional_layer(l);
+ }
+ if (l.type == CONNECTED && l.batch_normalize) {
+ denormalize_connected_layer(l);
+ }
+ if (l.type == GRU && l.batch_normalize) {
+ denormalize_connected_layer(*l.input_z_layer);
+ denormalize_connected_layer(*l.input_r_layer);
+ denormalize_connected_layer(*l.input_h_layer);
+ denormalize_connected_layer(*l.state_z_layer);
+ denormalize_connected_layer(*l.state_r_layer);
+ denormalize_connected_layer(*l.state_h_layer);
}
}
save_weights(net, outfile);
}
-void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
+layer normalize_layer(layer l, int n)
+{
+ int j;
+ l.batch_normalize=1;
+ l.scales = calloc(n, sizeof(float));
+ for(j = 0; j < n; ++j){
+ l.scales[j] = 1;
+ }
+ l.rolling_mean = calloc(n, sizeof(float));
+ l.rolling_variance = calloc(n, sizeof(float));
+ return l;
+}
+
+void normalize_net(char *cfgfile, char *weightfile, char *outfile)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
@@ -174,10 +236,58 @@
int i;
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
- if(l.type == CONVOLUTIONAL){
+ if(l.type == CONVOLUTIONAL && !l.batch_normalize){
+ net.layers[i] = normalize_layer(l, l.n);
+ }
+ if (l.type == CONNECTED && !l.batch_normalize) {
+ net.layers[i] = normalize_layer(l, l.outputs);
+ }
+ if (l.type == GRU && l.batch_normalize) {
+ *l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs);
+ *l.input_r_layer = normalize_layer(*l.input_r_layer, l.input_r_layer->outputs);
+ *l.input_h_layer = normalize_layer(*l.input_h_layer, l.input_h_layer->outputs);
+ *l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs);
+ *l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs);
+ *l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs);
+ net.layers[i].batch_normalize=1;
+ }
+ }
+ save_weights(net, outfile);
+}
+
+void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ if (weightfile) {
+ load_weights(&net, weightfile);
+ }
+ int i;
+ for (i = 0; i < net.n; ++i) {
+ layer l = net.layers[i];
+ if (l.type == CONVOLUTIONAL && l.batch_normalize) {
denormalize_convolutional_layer(l);
net.layers[i].batch_normalize=0;
}
+ if (l.type == CONNECTED && l.batch_normalize) {
+ denormalize_connected_layer(l);
+ net.layers[i].batch_normalize=0;
+ }
+ if (l.type == GRU && l.batch_normalize) {
+ denormalize_connected_layer(*l.input_z_layer);
+ denormalize_connected_layer(*l.input_r_layer);
+ denormalize_connected_layer(*l.input_h_layer);
+ denormalize_connected_layer(*l.state_z_layer);
+ denormalize_connected_layer(*l.state_r_layer);
+ denormalize_connected_layer(*l.state_h_layer);
+ l.input_z_layer->batch_normalize = 0;
+ l.input_r_layer->batch_normalize = 0;
+ l.input_h_layer->batch_normalize = 0;
+ l.state_z_layer->batch_normalize = 0;
+ l.state_r_layer->batch_normalize = 0;
+ l.state_h_layer->batch_normalize = 0;
+ net.layers[i].batch_normalize=0;
+ }
}
save_weights(net, outfile);
}
@@ -204,7 +314,9 @@
return 0;
}
gpu_index = find_int_arg(argc, argv, "-i", 0);
- if(find_arg(argc, argv, "-nogpu")) gpu_index = -1;
+ if(find_arg(argc, argv, "-nogpu")) {
+ gpu_index = -1;
+ }
#ifndef GPU
gpu_index = -1;
@@ -221,18 +333,36 @@
average(argc, argv);
} else if (0 == strcmp(argv[1], "yolo")){
run_yolo(argc, argv);
- } else if (0 == strcmp(argv[1], "swag")){
- run_swag(argc, argv);
+ } else if (0 == strcmp(argv[1], "voxel")){
+ run_voxel(argc, argv);
+ } else if (0 == strcmp(argv[1], "super")){
+ run_super(argc, argv);
+ } else if (0 == strcmp(argv[1], "detector")){
+ run_detector(argc, argv);
+ } else if (0 == strcmp(argv[1], "cifar")){
+ run_cifar(argc, argv);
+ } else if (0 == strcmp(argv[1], "go")){
+ run_go(argc, argv);
+ } else if (0 == strcmp(argv[1], "rnn")){
+ run_char_rnn(argc, argv);
+ } else if (0 == strcmp(argv[1], "vid")){
+ run_vid_rnn(argc, argv);
} else if (0 == strcmp(argv[1], "coco")){
run_coco(argc, argv);
} else if (0 == strcmp(argv[1], "classifier")){
run_classifier(argc, argv);
+ } else if (0 == strcmp(argv[1], "art")){
+ run_art(argc, argv);
+ } else if (0 == strcmp(argv[1], "tag")){
+ run_tag(argc, argv);
} else if (0 == strcmp(argv[1], "compare")){
run_compare(argc, argv);
} else if (0 == strcmp(argv[1], "dice")){
run_dice(argc, argv);
} else if (0 == strcmp(argv[1], "writing")){
run_writing(argc, argv);
+ } else if (0 == strcmp(argv[1], "3d")){
+ composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
} else if (0 == strcmp(argv[1], "test")){
test_resize(argv[2]);
} else if (0 == strcmp(argv[1], "captcha")){
@@ -243,14 +373,22 @@
change_rate(argv[2], atof(argv[3]), (argc > 4) ? atof(argv[4]) : 0);
} else if (0 == strcmp(argv[1], "rgbgr")){
rgbgr_net(argv[2], argv[3], argv[4]);
+ } else if (0 == strcmp(argv[1], "reset")){
+ reset_normalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "denormalize")){
denormalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "normalize")){
normalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "rescale")){
rescale_net(argv[2], argv[3], argv[4]);
+ } else if (0 == strcmp(argv[1], "ops")){
+ operations(argv[2]);
+ } else if (0 == strcmp(argv[1], "speed")){
+ speed(argv[2], (argc > 3) ? atoi(argv[3]) : 0);
} else if (0 == strcmp(argv[1], "partial")){
partial(argv[2], argv[3], argv[4], atoi(argv[5]));
+ } else if (0 == strcmp(argv[1], "average")){
+ average(argc, argv);
} else if (0 == strcmp(argv[1], "stacked")){
stacked(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "visualize")){
--
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